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- Benjamin Recht, Christopher Ré, Stephen J. Wright, Feng Niu
- NIPS
- 2011

Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms,… (More)

- Feng Niu, Christopher Ré, AnHai Doan, Jude W. Shavlik
- PVLDB
- 2011

Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to large real-world data sets, which is preventing their widespread… (More)

- Benjamin Recht, Christopher Ré
- Math. Program. Comput.
- 2013

This paper develops Jellyfish, an algorithm for solving data-processing problems with matrix-valued decision variables regularized to have low rank. Particular examples of problems solvable by Jellyfish include matrix completion problems and least-squares problems regular-ized by the nuclear norm or γ 2-norm. Jellyfish implements a projected incremental… (More)

- Victor Bittorf, Benjamin Recht, Christopher Ré, Joel A. Tropp
- NIPS
- 2012

This paper describes a new approach for computing nonnegative matrix factoriza-tions (NMFs) with linear programming. The key idea is a data-driven model for the factorization, in which the most salient features in the data are used to express the remaining features. More precisely, given a data matrix X, the algorithm identifies a matrix C that satisfies X… (More)

- Ji Liu, Stephen J. Wright, Christopher Ré, Victor Bittorf, Srikrishna Sridhar
- Journal of Machine Learning Research
- 2014

We describe an asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably constrained functions. The method achieves a linear convergence rate on functions that satisfy an essential strong convexity property and a sublinear rate (1/K) on general convex functions. Near-linear speedup on a multicore system… (More)

- Christopher Ré, Nilesh N. Dalvi, Dan Suciu
- 2007 IEEE 23rd International Conference on Data…
- 2007

Modern enterprise applications are forced to deal with unreliable, inconsistent and imprecise information. Probabilistic databases can model such data naturally, but SQL query evaluation on probabilistic databases is difficult: previous approaches have either restricted the SQL queries, or computed approximate probabilities, or did not scale, and it was… (More)

- Christopher Ré, Dan Suciu
- 2007

We study the evaluation of positive conjunctive queries with Boolean aggregate tests (similar to HAVING queries in SQL) on probabilistic databases. Our motivation is to handle aggregate queries over imprecise data resulting from information integration or information extraction. More precisely, we study con-junctive queries with predicate aggregates using… (More)

- Christopher De Sa, Christopher Ré, Kunle Olukotun
- ICML
- 2015

The Burer-Monteiro [1] decomposition (X = Y Y T) with stochastic gradient descent is commonly employed to speed up and scale up matrix problems including matrix completion, subspace tracking, and SDP relaxation. Although it is widely used in practice, there exist no known global convergence results for this method. In this paper, we prove that, under broad… (More)

- Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik
- Int. J. Semantic Web Inf. Syst.
- 2012

Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. We present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, we have implemented a solution to… (More)

- Jihad Boulos, Nilesh N. Dalvi, Bhushan Mandhani, Shobhit Mathur, Christopher Ré, Dan Suciu
- SIGMOD Conference
- 2005

MystiQ is a system that uses probabilistic query semantics [3] to find answers in large numbers of data sources of less than perfect quality. There are many reasons why the data originating from many different sources may be of poor quality, and therefore difficult to query: the same data item may have different representation in different sources; the… (More)